Information Retrieval

1. Information Retrieval Lecture 9

2. Outline • Map Reduce, cont. • Index compression • [Amazon Web Services]

3. Map Reduce • Map: (key, value) list(key’, value’) • Reduce: (key’, list(value’)  list (value’)

4. Example: counting occurrences of words in large collections of documents reduce(String key, Iterator values): // key: a word // values: a list of counts int result = 0; for each v in values: result += ParseInt(v); Emit(AsString(result)); map(String key, String value): // key: document name // value: document contents for each word w in value: EmitIntermediate(w, "1");

6. Other Map-Reducible problems • Distributed Grep: The map function emits a line if it matches a supplied pattern. The reduce function is an identity function that just copies the supplied intermediate data to the output. • Count of URL Access Frequency: The map function processes logs of web page requests and outputs ⟨URL, 1⟩. The reduce function adds together all values for the same URL and emits a ⟨URL, total count⟩ pair. • Reverse Web-Link Graph: The map function outputs ⟨target, source⟩ pairs for each link to a target URL found in a page named source. The reduce function concatenates the list of all source URLs associated with a given target URL and emits the pair: ⟨target, list(source)⟩

8. Compression: example • amendment allen-p/_sent_mail/465.:1:34 stclair-c/sent/993.:5:45,60,76,84,100 • Too verbose!

9. Compression: example • amendment allen-p/_sent_mail/465.:1:34 stclair-c/sent/993.:5:45,60,76,84,100(that’s 74 characters) • compare to • 34 1342:1:34 9745:5:45,60,76,84,100 (that’s 33 characters) need to keep a mapping between numbers and names